Triple
T22511892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Señor de Balaguer |
E556539
|
entity |
| Predicate | associatedWithTown |
P19735
|
FINISHED |
| Object | Balaguer |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Balaguer | Statement: [Señor de Balaguer, associatedWithTown, Balaguer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balaguer Context triple: [Señor de Balaguer, associatedWithTown, Balaguer]
-
A.
Balaguer
chosen
Balaguer is a historic town in Catalonia, Spain, known for its medieval heritage and strategic location along the Segre River.
-
B.
Víctor Balaguer
Víctor Balaguer was a prominent 19th-century Catalan writer, politician, and cultural leader who played a key role in the Catalan literary and national revival known as the Renaixença.
-
C.
Joaquín Balaguer
Joaquín Balaguer was a long-serving Dominican politician and statesman who served multiple terms as president of the Dominican Republic in the 20th century.
-
D.
Asunción Balaguer
Asunción Balaguer was a Spanish film, television, and stage actress known for her long and prolific career in Spanish cinema and theater.
-
E.
Luis Balaguer
Luis Balaguer is a television producer and entertainment executive known for his work on series such as "Killer Women."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e11e555edc81909ca803587dafd747 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15d61a27881909faed490d2b65f39 |
completed | April 29, 2026, 1:22 a.m. |
Created at: April 16, 2026, 8:50 p.m.